def main(args):
pp = Preprocessing()
# load data
print("Loading Data.....\n\n")
train_block, train_block_label = pp.read_train_file(
args.train_data, args.train_label)
test_block = pp.read_test_file(args.test_data)
# explore data, do some visualization
print("Exploring Data (see 'fig' folder for visualization) .....\n\n")
viz = Visualization()
# histogram for the lpc coefficient distribution
viz.visualize_lpc_distribution(train_block)
# histogram for the block length (or point of time) distribution
viz.visualize_block_length_distribution(train_block)
# plot one block of lpc coefficient for each speaker to look at the pattern of voice frequency
viz.visualize_lpc_time_series(train_block)
viz.visualize_fitted_lpc_series(train_block)
max_length = 29
final_block_size = 18
print("Data Preprocessing (padding to fixed size blocks)....\n\n")
# Take the best lengths (18), truncate the longer block, and pad the shorter block by the last row
train_data = pp.pad_to_fixed_size_blocks(train_block, max_length,
final_block_size)
test_data = pp.pad_to_fixed_size_blocks(test_block, max_length,
final_block_size)
# dummy test label for convenience
test_block_label = [[i] for i in np.zeros(len(test_data))]
print("Generating Features (for ML Algorithms)... \n\n")
# Generate fixed length feature vector for traditional machine learning input
final_train_data = pp.convert_to_vectors(train_data, train_block_label,
final_block_size)
final_test_data = pp.convert_to_vectors(test_data, test_block_label,
final_block_size)
# See scatter plot to find out if there is grouping based on feature vector
viz.lpc_scatter_plot(final_train_data)
# Looks like there is a grouping, so let's try to classify using some popular algorithm
model = Models()
model.run_classification_models(final_train_data, final_test_data)
print("SVM Prediction Saved (see 'results/submission.txt' )... \n\n")
#Also try LSTM for classification
model.run_LSTM_model(np.array(train_data), np.array(train_block_label),
np.array(test_data))
print("LSTM Prediction Saved (see 'results/submission_lstm.txt' )... \n\n")
